{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3d2d880b",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### 导入相关的库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "25b83a68",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings\n",
    "plt.rcParams['font.family']=['sans-serif']\n",
    "plt.rcParams['font.sans-serif']='SimHei'\n",
    "\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7f30dc2b",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x230eb253160>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "good = np.array([[95, 93], [90, 92], [91, 96]])\n",
    "medium = np.array([[85, 82], [83, 87], [80, 84]])\n",
    "bad = np.array([[61, 69], [66, 63], [72, 65]])\n",
    "unknown = np.array([[83, 77]])\n",
    "\n",
    "plt.scatter(good[:,0],good[:,1],color='r',label='优等生')\n",
    "plt.scatter(medium[:,0],medium[:,1],color='g',label='中等生')\n",
    "plt.scatter(bad[:,0],bad[:,1],color='b',label='差等生')\n",
    "plt.scatter(unknown[:,0],unknown[:,1],color='orange',marker='*',s=200,label='未知')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8cb303f",
   "metadata": {},
   "source": [
    "### 鸢尾花数据KNN分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "52ad38ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        13\n",
      "           1       0.78      0.44      0.56        16\n",
      "           2       0.44      0.78      0.56         9\n",
      "\n",
      "    accuracy                           0.71        38\n",
      "   macro avg       0.74      0.74      0.71        38\n",
      "weighted avg       0.77      0.71      0.71        38\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "iris=load_iris()\n",
    "x=iris.data[:,:2]\n",
    "y=iris.target\n",
    "# 切分数据集\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.25,random_state=0)\n",
    "knn=KNeighborsClassifier(n_neighbors=3,weights='uniform')\n",
    "knn.fit(x_train,y_train)\n",
    "y_hat=knn.predict(x_test)\n",
    "print(classification_report(y_test,y_hat))\n",
    "# precision 精确率 就是找的准, recall 召回率 就是搜索的全 f1就是精确率和召回率的调和平均 理想值为1 最差为0\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50703af0",
   "metadata": {},
   "source": [
    "### 超参数对模型的影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "aba7429c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "def plot_decision_boundary(model, X, y):\n",
    "    color=['r','g','b']\n",
    "    marker=['*','v','x']\n",
    "    class_label=np.unique(y)\n",
    "    cmap=ListedColormap(color[:len(class_label)])\n",
    "    x1_min,x2_min=np.min(X,axis=0)\n",
    "    x1_max,x2_max=np.max(X,axis=0)\n",
    "    x1=np.arange(x1_min-1,x1_max+1,0.02)\n",
    "    x2=np.arange(x2_min-1,x2_max+1,0.02)\n",
    "    X1,X2=np.meshgrid(x1,x2)\n",
    "    Z=model.predict(np.c_[X1.ravel(),X2.ravel()])\n",
    "    Z=Z.reshape(X1.shape)\n",
    "    plt.contourf(X1,X2,Z,xmap=cmap,alpha=0.5)\n",
    "    for i,class_ in enumerate(class_label):\n",
    "        plt.scatter(x=X[y==class_,0],y=X[y==class_,1],\n",
    "                   c=cmap.colors[i],label=class_,marker=marker[i])\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "9ccb3e7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from itertools import product\n",
    "weights = ['uniform', 'distance']\n",
    "ks = [2, 15]\n",
    "plt.figure(figsize=(18, 10))\n",
    "# 计算weights与ks的笛卡尔积组合。这样就可以使用单层循环取代嵌套循环，\n",
    "# 增加代码可读性与可理解性。\n",
    "for i, (w, k) in enumerate(product(weights, ks), start=1):\n",
    "    plt.subplot(2,2,i)\n",
    "    plt.title(f'K值:{k} 权重:{w}')\n",
    "    knn=KNeighborsClassifier(n_neighbors=k,weights=w)\n",
    "    knn.fit(x,y)\n",
    "    plot_decision_boundary(knn,x_train,y_train)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "77dc44fe",
   "metadata": {},
   "source": [
    "通过决策边界，我们可以得出如下结论：\n",
    "K的值越小，模型敏感度越强（稳定性越弱），模型也就越复杂，容易过拟合。\n",
    "K的值越大，模型敏感度越弱（稳定性越强），模型也就越简单，容易欠拟合"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0d7470c",
   "metadata": {},
   "source": [
    "### 超参数调整"
   ]
  },
  {
   "cell_type": "raw",
   "id": "238e8e70",
   "metadata": {},
   "source": [
    "实际应用中,很难单凭直觉就能找出合适的参数,通常利用网格交叉验证的方式,找出效果最好的超参数."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "c988c349",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 2 folds for each of 20 candidates, totalling 40 fits\n",
      "[CV 1/2; 1/20] START n_neighbors=1, weights=uniform.............................\n",
      "[CV 1/2; 1/20] END n_neighbors=1, weights=uniform;, score=0.679 total time=   0.0s\n",
      "[CV 2/2; 1/20] START n_neighbors=1, weights=uniform.............................\n",
      "[CV 2/2; 1/20] END n_neighbors=1, weights=uniform;, score=0.750 total time=   0.0s\n",
      "[CV 1/2; 2/20] START n_neighbors=1, weights=distance............................\n",
      "[CV 1/2; 2/20] END n_neighbors=1, weights=distance;, score=0.679 total time=   0.0s\n",
      "[CV 2/2; 2/20] START n_neighbors=1, weights=distance............................\n",
      "[CV 2/2; 2/20] END n_neighbors=1, weights=distance;, score=0.750 total time=   0.0s\n",
      "[CV 1/2; 3/20] START n_neighbors=2, weights=uniform.............................\n",
      "[CV 1/2; 3/20] END n_neighbors=2, weights=uniform;, score=0.679 total time=   0.0s\n",
      "[CV 2/2; 3/20] START n_neighbors=2, weights=uniform.............................\n",
      "[CV 2/2; 3/20] END n_neighbors=2, weights=uniform;, score=0.768 total time=   0.0s\n",
      "[CV 1/2; 4/20] START n_neighbors=2, weights=distance............................\n",
      "[CV 1/2; 4/20] END n_neighbors=2, weights=distance;, score=0.661 total time=   0.0s\n",
      "[CV 2/2; 4/20] START n_neighbors=2, weights=distance............................\n",
      "[CV 2/2; 4/20] END n_neighbors=2, weights=distance;, score=0.732 total time=   0.0s\n",
      "[CV 1/2; 5/20] START n_neighbors=3, weights=uniform.............................\n",
      "[CV 1/2; 5/20] END n_neighbors=3, weights=uniform;, score=0.661 total time=   0.0s\n",
      "[CV 2/2; 5/20] START n_neighbors=3, weights=uniform.............................\n",
      "[CV 2/2; 5/20] END n_neighbors=3, weights=uniform;, score=0.786 total time=   0.0s\n",
      "[CV 1/2; 6/20] START n_neighbors=3, weights=distance............................\n",
      "[CV 1/2; 6/20] END n_neighbors=3, weights=distance;, score=0.661 total time=   0.0s\n",
      "[CV 2/2; 6/20] START n_neighbors=3, weights=distance............................\n",
      "[CV 2/2; 6/20] END n_neighbors=3, weights=distance;, score=0.732 total time=   0.0s\n",
      "[CV 1/2; 7/20] START n_neighbors=4, weights=uniform.............................\n",
      "[CV 1/2; 7/20] END n_neighbors=4, weights=uniform;, score=0.679 total time=   0.0s\n",
      "[CV 2/2; 7/20] START n_neighbors=4, weights=uniform.............................\n",
      "[CV 2/2; 7/20] END n_neighbors=4, weights=uniform;, score=0.768 total time=   0.0s\n",
      "[CV 1/2; 8/20] START n_neighbors=4, weights=distance............................\n",
      "[CV 1/2; 8/20] END n_neighbors=4, weights=distance;, score=0.661 total time=   0.0s\n",
      "[CV 2/2; 8/20] START n_neighbors=4, weights=distance............................\n",
      "[CV 2/2; 8/20] END n_neighbors=4, weights=distance;, score=0.732 total time=   0.0s\n",
      "[CV 1/2; 9/20] START n_neighbors=5, weights=uniform.............................\n",
      "[CV 1/2; 9/20] END n_neighbors=5, weights=uniform;, score=0.679 total time=   0.0s\n",
      "[CV 2/2; 9/20] START n_neighbors=5, weights=uniform.............................\n",
      "[CV 2/2; 9/20] END n_neighbors=5, weights=uniform;, score=0.804 total time=   0.0s\n",
      "[CV 1/2; 10/20] START n_neighbors=5, weights=distance...........................\n",
      "[CV 1/2; 10/20] END n_neighbors=5, weights=distance;, score=0.679 total time=   0.0s\n",
      "[CV 2/2; 10/20] START n_neighbors=5, weights=distance...........................\n",
      "[CV 2/2; 10/20] END n_neighbors=5, weights=distance;, score=0.732 total time=   0.0s\n",
      "[CV 1/2; 11/20] START n_neighbors=6, weights=uniform............................\n",
      "[CV 1/2; 11/20] END n_neighbors=6, weights=uniform;, score=0.661 total time=   0.0s\n",
      "[CV 2/2; 11/20] START n_neighbors=6, weights=uniform............................\n",
      "[CV 2/2; 11/20] END n_neighbors=6, weights=uniform;, score=0.786 total time=   0.0s\n",
      "[CV 1/2; 12/20] START n_neighbors=6, weights=distance...........................\n",
      "[CV 1/2; 12/20] END n_neighbors=6, weights=distance;, score=0.679 total time=   0.0s\n",
      "[CV 2/2; 12/20] START n_neighbors=6, weights=distance...........................\n",
      "[CV 2/2; 12/20] END n_neighbors=6, weights=distance;, score=0.732 total time=   0.0s\n",
      "[CV 1/2; 13/20] START n_neighbors=7, weights=uniform............................\n",
      "[CV 1/2; 13/20] END n_neighbors=7, weights=uniform;, score=0.679 total time=   0.0s\n",
      "[CV 2/2; 13/20] START n_neighbors=7, weights=uniform............................\n",
      "[CV 2/2; 13/20] END n_neighbors=7, weights=uniform;, score=0.804 total time=   0.0s\n",
      "[CV 1/2; 14/20] START n_neighbors=7, weights=distance...........................\n",
      "[CV 1/2; 14/20] END n_neighbors=7, weights=distance;, score=0.679 total time=   0.0s\n",
      "[CV 2/2; 14/20] START n_neighbors=7, weights=distance...........................\n",
      "[CV 2/2; 14/20] END n_neighbors=7, weights=distance;, score=0.750 total time=   0.0s\n",
      "[CV 1/2; 15/20] START n_neighbors=8, weights=uniform............................\n",
      "[CV 1/2; 15/20] END n_neighbors=8, weights=uniform;, score=0.661 total time=   0.0s\n",
      "[CV 2/2; 15/20] START n_neighbors=8, weights=uniform............................\n",
      "[CV 2/2; 15/20] END n_neighbors=8, weights=uniform;, score=0.786 total time=   0.0s\n",
      "[CV 1/2; 16/20] START n_neighbors=8, weights=distance...........................\n",
      "[CV 1/2; 16/20] END n_neighbors=8, weights=distance;, score=0.696 total time=   0.0s\n",
      "[CV 2/2; 16/20] START n_neighbors=8, weights=distance...........................\n",
      "[CV 2/2; 16/20] END n_neighbors=8, weights=distance;, score=0.750 total time=   0.0s\n",
      "[CV 1/2; 17/20] START n_neighbors=9, weights=uniform............................\n",
      "[CV 1/2; 17/20] END n_neighbors=9, weights=uniform;, score=0.696 total time=   0.0s\n",
      "[CV 2/2; 17/20] START n_neighbors=9, weights=uniform............................\n",
      "[CV 2/2; 17/20] END n_neighbors=9, weights=uniform;, score=0.821 total time=   0.0s\n",
      "[CV 1/2; 18/20] START n_neighbors=9, weights=distance...........................\n",
      "[CV 1/2; 18/20] END n_neighbors=9, weights=distance;, score=0.696 total time=   0.0s\n",
      "[CV 2/2; 18/20] START n_neighbors=9, weights=distance...........................\n",
      "[CV 2/2; 18/20] END n_neighbors=9, weights=distance;, score=0.750 total time=   0.0s\n",
      "[CV 1/2; 19/20] START n_neighbors=10, weights=uniform...........................\n",
      "[CV 1/2; 19/20] END n_neighbors=10, weights=uniform;, score=0.696 total time=   0.0s\n",
      "[CV 2/2; 19/20] START n_neighbors=10, weights=uniform...........................\n",
      "[CV 2/2; 19/20] END n_neighbors=10, weights=uniform;, score=0.821 total time=   0.0s\n",
      "[CV 1/2; 20/20] START n_neighbors=10, weights=distance..........................\n",
      "[CV 1/2; 20/20] END n_neighbors=10, weights=distance;, score=0.696 total time=   0.0s\n",
      "[CV 2/2; 20/20] START n_neighbors=10, weights=distance..........................\n",
      "[CV 2/2; 20/20] END n_neighbors=10, weights=distance;, score=0.750 total time=   0.0s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=2, estimator=KNeighborsClassifier(),\n",
       "             param_grid={'n_neighbors': range(1, 11),\n",
       "                         'weights': ['uniform', 'distance']},\n",
       "             scoring='accuracy', verbose=10)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV  \n",
    "\n",
    "knn=KNeighborsClassifier()\n",
    "grid={\"n_neighbors\": range(1,11,1),\"weights\":['uniform','distance']}\n",
    "# estimator 评估器,即对哪个模型调整参数\n",
    "# param_grid :需要检验的超参数组合.从这些组合中,寻找效果最好的超参数组合\n",
    "# scoring模型评估标准\n",
    "# n_jobs并发数量\n",
    "# cv 交叉验证数\n",
    "# verbose 输出冗余信息,值越大,输出的信息越多\n",
    "gs=GridSearchCV(estimator=knn,param_grid=grid,scoring='accuracy',cv=2,verbose=10)\n",
    "gs.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52197883",
   "metadata": {},
   "outputs": [],
   "source": [
    "玩个交叉验证后,可以通过GridSearchCV的对象的相关属性,来获取最好的参数,分值和模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "02dd586b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7589285714285714\n",
      "{'n_neighbors': 9, 'weights': 'uniform'}\n",
      "KNeighborsClassifier(n_neighbors=9)\n"
     ]
    }
   ],
   "source": [
    "# 最好的分值\n",
    "print(gs.best_score_)\n",
    "# 最好的超参数组合\n",
    "print(gs.best_params_)\n",
    "# 最好的超参数训练的模型\n",
    "print(gs.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "e7cb8b69",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        13\n",
      "           1       0.75      0.38      0.50        16\n",
      "           2       0.41      0.78      0.54         9\n",
      "\n",
      "    accuracy                           0.68        38\n",
      "   macro avg       0.72      0.72      0.68        38\n",
      "weighted avg       0.76      0.68      0.68        38\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 获取到超参数的最合适的数值后,最终实现最后的检验\n",
    "estimator=gs.best_estimator_\n",
    "y_hat=estimator.predict(x_test)\n",
    "print(classification_report(y_test,y_hat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5de296c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0fd12d8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13569888",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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